Effectiveness and implementation of a text messaging intervention to reduce depression and anxiety symptoms among Latinx and Non-Latinx white users during the COVID-19 pandemic

Text messaging interventions are increasingly used to help people manage depression and anxiety. However, little is known about the effectiveness and implementation of these interventions among U.S. Latinxs, who often face barriers to using mental health tools. The StayWell at Home (StayWell) intervention, a 60-day text messaging program based on cognitive behavioral therapy (CBT), was developed to help adults cope with depressive and anxiety symptoms during the COVID-19 pandemic. StayWell users (n = 398) received daily mood inquiries and automated skills-based text messages delivering CBT-informed coping strategies from an investigator-generated message bank. We conduct a Hybrid Type 1 mixed-methods study to compare the effectiveness and implementation of StayWell for Latinx and Non-Latinx White (NLW) adults using the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework. Effectiveness was measured using the PHQ-8 depression and GAD-7 anxiety scales, assessed before starting and after completing StayWell. Guided by RE-AIM, we conducted a thematic text analysis of responses to an open-ended question about user experiences to help contextualize quantitative findings. Approximately 65.8% (n = 262) of StayWell users completed pre-and-post surveys. On average, depressive (−1.48,p=.001) and anxiety (−1.38, p=.001) symptoms decreased from pre-to-post StayWell. Compared to NLW users (n = 192), Latinx users (n = 70) reported an additional −1.45 point (p < 0.05) decline in depressive symptoms, adjusting for demographics. Although Latinxs reported StayWell as relatively less useable (76.8 vs. 83.9, p=.001) than NLWs, they were more interested in continuing the program (7.5 vs. 6.2 out of 10, p=.001) and recommending it to a family member/friend (7.8 vs. 7.0 out of 10, p=.01). Based on the thematic analysis, both Latinx and NLW users enjoyed responding to mood inquiries and sought bi-directional, personalized text messages and texts with links to more information to resources. Only NLW users stated that StayWell provided no new information than they already knew from therapy or other sources. In contrast, Latinx users suggested that engagement with a behavioral provider through text or support groups would be beneficial, highlighting this group's unmet need for behavioral health care. mHealth interventions like StayWell are well-positioned to address population-level disparities by serving those with the greatest unmet needs if they are culturally adapted and actively disseminated to marginalized groups. Trial registration ClinicalTrials.gov Identifier: NCT04473599.

Telehealth Use, Care Continuity, and Quality: Diabetes and Hypertension Care in Community Health Centers Before and During the COVID-19 Pandemic.

BACKGROUND: Community health centers (CHCs) pivoted to using telehealth to deliver chronic care during the coronavirus COVID-19 pandemic. While care continuity can improve care quality and patients' experiences, it is unclear whether telehealth supported this relationship. OBJECTIVE: We examine the association of care continuity with diabetes and hypertension care quality in CHCs before and during COVID-19 and the mediating effect of telehealth. RESEARCH DESIGN: This was a cohort study. PARTICIPANTS: Electronic health record data from 166 CHCs with n=20,792 patients with diabetes and/or hypertension with ≥2 encounters/year during 2019 and 2020. METHODS: Multivariable logistic regression models estimated the association of care continuity (Modified Modified Continuity Index; MMCI) with telehealth use and care processes. Generalized linear regression models estimated the association of MMCI and intermediate outcomes. Formal mediation analyses assessed whether telehealth mediated the association of MMCI with A1c testing during 2020. RESULTS: MMCI [2019: odds ratio (OR)=1.98, marginal effect=0.69, z=165.50, P<0.001; 2020: OR=1.50, marginal effect=0.63, z=147.73, P<0.001] and telehealth use (2019: OR=1.50, marginal effect=0.85, z=122.87, P<0.001; 2020: OR=10.00, marginal effect=0.90, z=155.57, P<0.001) were associated with higher odds of A1c testing. MMCI was associated with lower systolic (ß=-2.90, P<0.001) and diastolic blood pressure (ß=-1.44, P<0.001) in 2020, and lower A1c values (2019: ß=-0.57, P=0.007; 2020: ß=-0.45, P=0.008) in both years. In 2020, telehealth use mediated 38.7% of the relationship between MMCI and A1c testing. CONCLUSIONS: Higher care continuity is associated with telehealth use and A1c testing, and lower A1c and blood pressure. Telehealth use mediates the association of care continuity and A1c testing. Care continuity may facilitate telehealth use and resilient performance on process measures.

Frontline work and racial disparities in social and economic pandemic stressors during the first COVID-19 surge.

OBJECTIVE: To assess the magnitude of racial-ethnic disparities in pandemic-related social stressors and examine frontline work's moderating relationship on these stressors. DATA SOURCES: Employed Californians' responses to the Institute for Governmental Studies (IGS) poll from April 16-20, 2020, were analyzed. The Pandemic Stressor Scale (PSS) assessed the extent to which respondents experienced or anticipated problems resulting from the inability to pay for basic necessities, job instability, lacking paid sick leave, unavailability of childcare, and reduced wages or work hours due to COVID-19. STUDY DESIGN: Mixed-effects generalized linear models estimated (1) racial-ethnic disparities in pandemic stressors among workers during the first COVID-19 surge, adjusting for covariates, and (2) tested the interaction between race-ethnicity and frontline worker status, which includes a subset of essential workers who must perform their job on-site, to assess differential associations of frontline work by race-ethnicity. DATA COLLECTION: The IGS poll data from employed workers (n = 4795) were linked to the 2018 Centers for Disease Control and Prevention Social Vulnerability Index at the zip code level (N = 1068). PRINCIPAL FINDINGS: The average PSS score was 37.34 (SD = 30.49). Whites had the lowest PSS score (29.88, SD = 26.52), and Latinxs had the highest (50.74, SD = 32.61). In adjusted analyses, Black frontline workers reported more pandemic-related stressors than White frontline workers (PSS = 47.73 vs. 36.96, p < 0.001). Latinxs reported more pandemic stressors irrespective of frontline worker status. However, the 5.09-point difference between Latinx frontline and non-frontline workers was not statistically different from the 4.6-point disparity between White frontline and non-frontline workers. CONCLUSION: Latinx workers and Black frontline workers disproportionately reported pandemic-related stressors. To reduce stress on frontline workers during crises, worker protections like paid sick leave, universal access to childcare, and improved job security are needed, particularly for those disproportionately affected by structural inequities, such as racially minoritized populations.

A Text Messaging Intervention (StayWell at Home) to Counteract Depression and Anxiety During COVID-19 Social Distancing: Pre-Post Study.

BACKGROUND: Social distancing and stay-at-home orders are critical interventions to slow down person-to-person transmission of COVID-19. While these societal changes help contain the pandemic, they also have unintended negative consequences, including anxiety and depression. We developed StayWell, a daily skills-based SMS text messaging program, to mitigate COVID-19-related depression and anxiety symptoms among people who speak English and Spanish in the United States. OBJECTIVE: This paper describes the changes in StayWell participants' anxiety and depression levels after 60 days of exposure to skills-based SMS text messages. METHODS: We used self-administered, empirically supported web-based questionnaires to assess the demographic and clinical characteristics of StayWell participants. Anxiety and depression were measured using the 2-item Generalized Anxiety Disorder (GAD-2) scale and the 8-item Patient Health Questionnaire-8 (PHQ-8) scale at baseline and 60-day timepoints. We used 2-tailed paired t tests to detect changes in PHQ-8 and GAD-2 scores from baseline to follow-up measured 60 days later. RESULTS: The analytic sample includes 193 participants who completed both the baseline and 60-day exit questionnaires. At the 60-day time point, there were significant reductions in both PHQ-8 and GAD-2 scores from baseline. We found an average reduction of -1.72 (95% CI -2.35 to -1.09) in PHQ-8 scores and -0.48 (95% CI -0.71 to -0.25) in GAD-2 scores. These improvements translated to an 18.5% and 17.2% reduction in mean PHQ-8 and GAD-2 scores, respectively. CONCLUSIONS: StayWell is an accessible, low-intensity population-level mental health intervention. Participation in StayWell focused on COVID-19 mental health coping skills and was related to improved depression and anxiety symptoms. In addition to improvements in outcomes, we found high levels of engagement during the 60-day intervention period. Text messaging interventions could serve as an important public health tool for disseminating strategies to manage mental health. TRIAL REGISTRATION: ClinicalTrials.gov NCT04473599; https://clinicaltrials.gov/ct2/show/NCT04473599. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/23592.

The need for feminist intersectionality in digital health.

Digital health, including the use of mobile health apps, telemedicine, and data analytics to improve health systems, has surged during the COVID-19 pandemic. The social and economic fallout from COVID-19 has further exacerbated gender inequities, through increased domestic violence against women, soaring unemployment rates in women, and increased unpaid familial care taken up by women-all factors that can worsen women's health. Digital health can bolster gender equity through increased access to health care, empowerment of one's own health data, and reduced burden of unpaid care work. Yet, digital health is rarely designed from a gender equity perspective. In this Viewpoint, we show that because of lower access and exclusion from app design, gender imbalance in digital health leadership, and harmful gender stereotypes, digital health is disadvantaging women-especially women with racial or ethnic minority backgrounds. Tackling digital health's gender inequities is more crucial than ever. We explain our feminist intersectionality framework to tackle digital health's gender inequities and provide recommendations for future research.

Conducting Internet-Based Visits for Onboarding Populations With Limited Digital Literacy to an mHealth Intervention: Development of a Patient-Centered Approach.

BACKGROUND: The COVID-19 pandemic has propelled patient-facing research to shift to digital and telehealth strategies. If these strategies are not adapted for minority patients of lower socioeconomic status, health inequality will further increase. Patient-centered models of care can successfully improve access and experience for minority patients. OBJECTIVE: This study aims to present the development process and preliminary acceptability of altering in-person onboarding procedures into internet-based, remote procedures for a mobile health (mHealth) intervention in a population with limited digital literacy. METHODS: We actively recruited safety-net patients (English- and Spanish-speaking adults with diabetes and depression who were receiving care at a public health care delivery system in San Francisco, United States) into a randomized controlled trial of text messaging support for physical activity. Because of the COVID-19 pandemic, we modified the in-person recruitment and onboarding procedures to internet-based, remote processes with human support. We conducted a preliminary evaluation of how the composition of the recruited cohort might have changed from the pre-COVID-19 period to the COVID-19 enrollment period. First, we analyzed the digital profiles of patients (n=32) who had participated in previous in-person onboarding sessions prior to the COVID-19 pandemic. Next, we documented all changes made to our onboarding processes to account for remote recruitment, especially those needed to support patients who were not very familiar with downloading apps onto their mobile phones on their own. Finally, we used the new study procedures to recruit patients (n=11) during the COVID-19 social distancing period. These patients were also asked about their experience enrolling into a fully digitized mHealth intervention. RESULTS: Recruitment across both pre-COVID-19 and COVID-19 periods (N=43) demonstrated relatively high rates of smartphone ownership but lower self-reported digital literacy, with 32.6% (14/43) of all patients reporting they needed help with using their smartphone and installing apps. Significant changes were made to the onboarding procedures, including facilitating app download via Zoom video call and/or a standard phone call and implementing brief, one-on-one staff-patient interactions to provide technical assistance personalized to each patient's digital literacy skills. Comparing recruitment during pre-COVID-19 and COVID-19 periods, the proportion of patients with digital literacy barriers reduced from 34.4% (11/32) in the pre-COVID-19 cohort to 27.3% (3/11) in the COVID-19 cohort. Differences in digital literacy scores between both cohorts were not significant (P=.49). CONCLUSIONS: Patients of lower socioeconomic status have high interest in using digital platforms to manage their health, but they may require additional upfront human support to gain access. One-on-one staff-patient partnerships allowed us to provide unique technical assistance personalized to each patient's digital literacy skills, with simple strategies to troubleshoot patient barriers upfront. These additional remote onboarding strategies can mitigate but not eliminate digital barriers for patients without extensive technology experience. TRIAL REGISTRATION: Clinicaltrials.gov NCT0349025, https://clinicaltrials.gov/ct2/show/NCT03490253.

A Text Messaging Intervention for Coping With Social Distancing During COVID-19 (StayWell at Home): Protocol for a Randomized Controlled Trial.

BACKGROUND: Social distancing is a crucial intervention to slow down person-to-person transmission of COVID-19. However, social distancing has negative consequences, including increases in depression and anxiety. Digital interventions, such as text messaging, can provide accessible support on a population-wide scale. We developed text messages in English and Spanish to help individuals manage their depressive mood and anxiety during the COVID-19 pandemic. OBJECTIVE: In a two-arm randomized controlled trial, we aim to examine the effect of our 60-day text messaging intervention. Additionally, we aim to assess whether the use of machine learning to adapt the messaging frequency and content improves the effectiveness of the intervention. Finally, we will examine the differences in daily mood ratings between the message categories and time windows. METHODS: The messages were designed within two different categories: behavioral activation and coping skills. Participants will be randomized into (1) a random messaging arm, where message category and timing will be chosen with equal probabilities, and (2) a reinforcement learning arm, with a learned decision mechanism for choosing the messages. Participants in both arms will receive one message per day within three different time windows and will be asked to provide their mood rating 3 hours later. We will compare self-reported daily mood ratings; self-reported depression, using the 8-item Patient Health Questionnaire; and self-reported anxiety, using the 7-item Generalized Anxiety Disorder scale at baseline and at intervention completion. RESULTS: The Committee for the Protection of Human Subjects at the University of California Berkeley approved this study in April 2020 (No. 2020-04-13162). Data collection began in April 2020 and will run to April 2021. As of August 24, 2020, we have enrolled 229 participants. We plan to submit manuscripts describing the main results of the trial and results from the microrandomized trial for publication in peer-reviewed journals and for presentations at national and international scientific meetings. CONCLUSIONS: Results will contribute to our knowledge of effective psychological tools to alleviate the negative effects of social distancing and the benefit of using machine learning to personalize digital mental health interventions. TRIAL REGISTRATION: ClinicalTrials.gov NCT04473599; https://clinicaltrials.gov/ct2/show/NCT04473599. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/23592.